IDEAS Scientific Programming Workshop, May 15, 2019

Visualization of Global MERS Cases

This report contains analysis and visualization of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) cases from Andrew Rambaut’s publicly available github (https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv).

The dataset is actually quite messy, requiring several cleaning procedures. Here we correct for different date types and create the variable that shows the day from the onset of the epidemic, epi.day.

mers <- read.csv('cases.csv') #load data

# correct errors
mers$hospitalized[890] <- c('2015-02-20')
mers <- mers[-417,]
mers$onset2 <- ymd(mers$onset)
mers$hospitalized2 <- ymd(mers$hospitalized)

# calculate days from onset of epidemic 
day0 <- min(na.omit(mers$onset2)) 
# Q1 would get errors for na, as many rows without onset date
mers$epi.day <- as.numeric(mers$onset2 - day0) # create epidemic day value 
# Q2 changes the class from diff in time to a numeric

Now that our data include the variables we want, in the format we want, we can visualise several different relationships.

# Plot 
ggplot(data=mers) + 
  geom_bar(aes(x=epi.day, fill = country), position = "fill") + 
  labs(x  = 'Epidemic day', y = 'Case count', 
  title = 'Global count of MERS cases by date of symptom onset', 
  caption = "Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv") + 
  coord_flip() 

mers$infectious.period <- mers$hospitalized2-mers$onset2 #calculate "raw" infectious period 
mers$infectious.period <- as.numeric(mers$infectious.period, units = "days") # convert to days
ggplot(data = mers) + 
  geom_histogram(aes(x = infectious.period)) +
  labs(x = 'Infectious period', y = 'Frequency', 
       title = 'Distribution of calculated MERS infectious period', 
       caption="Data from: https://github/rambaut/MERS-cases/blob/gh-pages/data/cases.csv")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Histogram plot
mers$infectious.period2 <- ifelse(mers$infectious.period < 0 , 0, mers$infectious.period)
ggplot(data = mers)+
  geom_histogram(aes(x=infectious.period2)) +
  labs(x = 'Infectious period', y = 'Frequency', 
       title = 'Distribution of calculated MERS infectious period (positive values only)', 
       caption = "Data from: https://github/rambaut/MERS-cases/blob/gh-pages/data/cases.csv")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Density plot
ggplot(data = mers)+
  geom_density(aes(x=infectious.period2)) +
  labs(x = 'Infectious period', y = 'Frequency', 
       title = 'Distribution of calculated MERS infectious period (positive values only)', 
       caption = "Data from: https://github/rambaut/MERS-cases/blob/gh-pages/data/cases.csv")

# Area plot
ggplot(data = mers)+
  geom_area(stat = 'bin', aes(x=infectious.period2)) +
  labs(x = 'Infectious period', y = 'Frequency', 
       title = 'Distribution of calculated MERS infectious period (positive values only)', 
       caption = "Data from: https://github/rambaut/MERS-cases/blob/gh-pages/data/cases.csv")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Exercise 3 
ggplot(mers, aes(x=infectious.period2, fill = country))+
  geom_dotplot(binaxis = "x", stackdir = "up", dotsize = 0.5) +
  labs(x = 'Infectious period', y = 'Frequency', 
       title = 'Distribution of calculated MERS infectious period (positive values only)', 
       caption = "Data from: https://github/rambaut/MERS-cases/blob/gh-pages/data/cases.csv")
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

# Exercise 4 
ggplot(mers, aes(y = infectious.period2, x = epi.day)) +
         geom_point(aes(colour = country))

# Exercise 5, add a smooth curve fit for the total data
ggplot(mers, aes(y = infectious.period2, x = epi.day)) +
  geom_point(aes(colour = country)) + geom_smooth(method = "loess")

# Exercise 6, plot infect period against time with smooth by country
ggplot(mers, aes(y = infectious.period2, x = epi.day)) +
  geom_point(aes(colour = country)) + geom_smooth(aes(group = country, colour = country), method = "loess")

# Faceting
ggplot(mers, aes( x = epi.day, y = infectious.period2)) +
  geom_point(aes(colour = country)) + 
  facet_wrap(~country) + 
  scale_y_continuous(limits = c(0,50)) +
  labs(x = 'Epidemic Day', y = 'Infectious period', 
       title = 'MERS infectious period (positive values only) over time', 
       caption = "Data from: https://github/rambaut/MERS-cases/blob/gh-pages/data/cases.csv")

ggplot(subset(mers, gender %in% c('M', 'F') & country %in% c('KSA', 'Oman', 'Iran', 'Jordan', 'Qatar', 'South Korea', 'UAE'))) +
  geom_point(aes(x = epi.day, y = infectious.period2, colour = country)) +
  facet_grid(gender ~ country) + 
  scale_y_continuous(limits = c(0,50)) +
  labs(x = 'Epidemic Day', y = 'Infectious period', 
        title = 'MERS infectious period by gender and country', 
        caption = "Data from: https://github/rambaut/MERS-cases/blob/gh-pages/data/cases.csv")

# Exercise 7, study variation in case fatality rate (the fraction of cases that end in death) over time and across countries 
mers$case_died <- ifelse(mers$outcome %in% c("?fatal", "fatal", "fatal?"), "Yes", "No")


cf_plot <- ggplot(mers) +
  geom_bar(aes(x = epi.day, colour = case_died), position = "fill") + 
  facet_wrap(~country) + 
  scale_y_continuous(limits = c(0,1)) +
  scale_colour_manual(values = c( "blue", "red")) +
  labs(x = 'Epidemic Day', y = 'Case fatality', 
       title = 'MERS case fatality over time by country', 
       caption = "Data from: https://github/rambaut/MERS-cases/blob/gh-pages/data/cases.csv")
cf_plot

mers_country <- mers %>% group_by(country, epi.day, case_died) %>% summarise(cases = n())
mers_country <- spread(mers_country, key = case_died, value = cases)
mers_country$Yes <- ifelse(is.na(mers_country$Yes) == TRUE, 0, mers_country$Yes)
mers_country$No <- ifelse(is.na(mers_country$No) == TRUE, 0, mers_country$No)
mers_country$case.fatality <- mers_country$Yes / (mers_country$Yes + mers_country$No)

For the next exercise, we need to use the a ggplot extension. I’ve chosen to use the ggridges package. ggridges provides visualizations ideal for our variable of interest, epi.day.

# Exercise 8:
cf2_plot <- ggplot(mers_country, aes(x = epi.day, y = country, fill = case.fatality)) + 
  geom_density_ridges() +
  scale_fill_continuous() # values are not currently colored
cf2_plot
## Picking joint bandwidth of 99.6

cf_plot2 <- ggplot(mers, aes(x = epi.day, y = country, fill = case_died)) + 
  geom_density_ridges_gradient() +
  scale_fill_manual(values = c("blue", "red"))
cf_plot2 
## Picking joint bandwidth of 104

We can make these plots interactive, using the plotly package.

# Use plotly
library(plotly)
epi.curve <- ggplot(mers)+ 
  geom_bar(aes(x = epi.day)) +
  labs(x = 'Epidemic day', y = 'Case count', title ='Global count of MERS cases by date of symptoms onset', 
       caption = "Data from: https://github/rambaut/MERS-cases/blob/gh-pages/data/cases.csv")
ggplotly(epi.curve)
# Exercise 9 
ggplotly(cf_plot)